Synthetic Intelligence for Human-Machine Collaboration: A Comprehensive Review

Abstract

The rapid advancement of Artificial Intelligence (AI) has enabled machines to perform complex tasks efficiently, yet most systems remain narrow, reactive, and rule based. Synthetic Intelligence (SI) introduces a new paradigm focused on creating genuine, adaptive intelligence rather than merely simulating human behavior. This review examines SI’s potential to enhance human–machine collaboration across domains such as healthcare (diagnostic and surgical assistants), industry (adaptive co-bots), defense (decision support), and education (personalized learning). Unlike conventional AI, SI enables machines to understand context, learn dynamically, and operate effectively in uncertain scenarios, complementing human abilities. The paper also addresses key challenges—trust, interpretability, safety, and ethics—and reviews current research, cognitive architectures, and brain-inspired models, emphasizing SI’s promise in developing adaptive, trustworthy, and cooperative intelligent systems for next-generation human–machine synergy.

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